AI Apology: A Critical Review of Apology in AI Systems
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913621225766912 |
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| author | Harland, Hadassah Dazeley, Richard Senaratne, Hashini Vamplew, Peter Cruz, Francisco Nakisa, Bahareh |
| author_facet | Harland, Hadassah Dazeley, Richard Senaratne, Hashini Vamplew, Peter Cruz, Francisco Nakisa, Bahareh |
| contents | Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value in human-machine interactions. Until recently, contributions to this area were sparse, and these works have yet to be synthesised into a cohesive body of knowledge. This article provides the first synthesis and critical analysis of the state of AI apology research, focusing on studies published between 2020 and 2023. We derive a framework of attributes to describe five core elements of apology: outcome, interaction, offence, recipient, and offender. With this framework as the basis for our critique, we show how apologies can be used to recover from misalignment in human-AI interactions, and examine trends and inconsistencies within the field. Among the observations, we outline the importance of curating a human-aligned and cross-disciplinary perspective in this research, with consideration for improved system capabilities and long-term outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Apology: A Critical Review of Apology in AI Systems Harland, Hadassah Dazeley, Richard Senaratne, Hashini Vamplew, Peter Cruz, Francisco Nakisa, Bahareh Computers and Society Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value in human-machine interactions. Until recently, contributions to this area were sparse, and these works have yet to be synthesised into a cohesive body of knowledge. This article provides the first synthesis and critical analysis of the state of AI apology research, focusing on studies published between 2020 and 2023. We derive a framework of attributes to describe five core elements of apology: outcome, interaction, offence, recipient, and offender. With this framework as the basis for our critique, we show how apologies can be used to recover from misalignment in human-AI interactions, and examine trends and inconsistencies within the field. Among the observations, we outline the importance of curating a human-aligned and cross-disciplinary perspective in this research, with consideration for improved system capabilities and long-term outcomes. |
| title | AI Apology: A Critical Review of Apology in AI Systems |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2412.15787 |